Prediction of bus arrival time is an important part of intelligent transportation systems. Accurate prediction can help passengers make travel plans and improve travel efficiency. Given the nonlinearity, randomness, and complexity of bus arrival time, this paper proposes the use of a wavelet neural network (WNN) model with an improved particle swarm optimization algorithm (IPSO) that replaces the gradient descent method. The proposed IPSO-WNN model overcomes the limitations of the gradient-based WNN which can easily produce local optimum solutions and stop the training process and thus improves prediction accuracy. Application of the model is illustrated using operational data of an actual bus line. The results show that the proposed model is capable of accurately predicting bus arrival time, where the root-mean square error and the maximum relative error were reduced by 42% and 49%, respectively.
Bus Arrival Time Prediction Using Wavelet Neural Network Trained by Improved Particle Swarm Optimization
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Expressway travel time prediction method based on particle swarm optimization wavelet neural network
European Patent Office | 2020
|Particle swarm optimization trained neural network for aquifer parameter estimation
Online Contents | 2012
|Particle swarm optimization trained neural network for aquifer parameter estimation
Springer Verlag | 2012
|Traffic flow prediction method based on particle swarm optimization neural network
European Patent Office | 2020
|